EP4260533A1 - Détermination d'une évaluation d'un ensemble de données - Google Patents

Détermination d'une évaluation d'un ensemble de données

Info

Publication number
EP4260533A1
EP4260533A1 EP22705728.8A EP22705728A EP4260533A1 EP 4260533 A1 EP4260533 A1 EP 4260533A1 EP 22705728 A EP22705728 A EP 22705728A EP 4260533 A1 EP4260533 A1 EP 4260533A1
Authority
EP
European Patent Office
Prior art keywords
data
determined
bew
info
source
Prior art date
Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
Pending
Application number
EP22705728.8A
Other languages
German (de)
English (en)
Inventor
Marco Kiehle
Markus Rascher
Current Assignee (The listed assignees may be inaccurate. Google has not performed a legal analysis and makes no representation or warranty as to the accuracy of the list.)
Siemens AG
Original Assignee
Siemens AG
Priority date (The priority date is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the date listed.)
Filing date
Publication date
Application filed by Siemens AG filed Critical Siemens AG
Publication of EP4260533A1 publication Critical patent/EP4260533A1/fr
Pending legal-status Critical Current

Links

Classifications

    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04L—TRANSMISSION OF DIGITAL INFORMATION, e.g. TELEGRAPHIC COMMUNICATION
    • H04L63/00—Network architectures or network communication protocols for network security
    • H04L63/08—Network architectures or network communication protocols for network security for authentication of entities
    • H04L63/0823—Network architectures or network communication protocols for network security for authentication of entities using certificates
    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04L—TRANSMISSION OF DIGITAL INFORMATION, e.g. TELEGRAPHIC COMMUNICATION
    • H04L41/00—Arrangements for maintenance, administration or management of data switching networks, e.g. of packet switching networks
    • H04L41/16—Arrangements for maintenance, administration or management of data switching networks, e.g. of packet switching networks using machine learning or artificial intelligence
    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04L—TRANSMISSION OF DIGITAL INFORMATION, e.g. TELEGRAPHIC COMMUNICATION
    • H04L43/00—Arrangements for monitoring or testing data switching networks
    • H04L43/02—Capturing of monitoring data
    • H04L43/028—Capturing of monitoring data by filtering
    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04L—TRANSMISSION OF DIGITAL INFORMATION, e.g. TELEGRAPHIC COMMUNICATION
    • H04L43/00—Arrangements for monitoring or testing data switching networks
    • H04L43/04—Processing captured monitoring data, e.g. for logfile generation
    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04L—TRANSMISSION OF DIGITAL INFORMATION, e.g. TELEGRAPHIC COMMUNICATION
    • H04L63/00—Network architectures or network communication protocols for network security
    • H04L63/14—Network architectures or network communication protocols for network security for detecting or protecting against malicious traffic
    • H04L63/1408—Network architectures or network communication protocols for network security for detecting or protecting against malicious traffic by monitoring network traffic
    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04W—WIRELESS COMMUNICATION NETWORKS
    • H04W12/00—Security arrangements; Authentication; Protecting privacy or anonymity
    • H04W12/60—Context-dependent security
    • H04W12/63—Location-dependent; Proximity-dependent

Definitions

  • the invention relates to determining an evaluation of a data set of a data packet, in particular based on one or more characteristics of the data set.
  • this invention describes a technical solution in the form of a computer-implemented method, with the aid of which the evaluation BEW of data can be determined in a reproducible manner. Accordingly, the method is used to determine such an evaluation BEW of a data set DS of a data package made available to a client by a data source. ketes D.
  • the evaluation BEW of the data record DS is determined with the aid of corresponding information from the past that is already available.
  • At least one of the characteristics CHAR1 to be determined in the first method step VI is a data pattern DSMUS.
  • data patterns can Error codes or certain temporal behavior within certain time windows.
  • the data packet D includes the data record DS to be evaluated and at least one output parameter DA of the data source.
  • At least one of the characteristics CHAR2 to be determined in the first method step VI is based on a context DSKXT of the data record DS, the context DSKXT being determined in a step V1_SEL_KXT of a selection method step V1_SEL of the first method step VI based on the at least one output parameter of the data source.
  • the assessment BEW of the data record DS is hereby carried out in the second method step V2 based on the identified data pattern DSMUS and on the determined context DSKXT and can, for example. can also be determined as that evaluation BEW, which can be determined by an appropriately trained artificial neuronal Network is output when playing the context DSKXT and the data pattern DSMUS.
  • At least one of the characteristics CHAR2 to be determined in the first method step can represent a suitable information source INFO_KXT for determining the evaluation BEW, which is set up to Assign data pattern DMUS_m an evaluation BEW, wherein the appropriate information source INFO_KXT is determined in a step V1_SEL_INFO of a selection process step V1_SEL of the first process step VI based on the at least one output parameter of the data source.
  • the evaluation BEW of the data record DS then takes place in the second method step V2 based on the previously identified data pattern DSMUS and on the determined suitable information source INFO_KXT and is thereby determined as the one evaluation BEW that is assigned to the identified data pattern DSMUS by the suitable information source INFO_KXT becomes . It is fundamentally conceivable that several sources of information are selected, so that more than one source of information is included when determining the evaluation.
  • step V1_SEL of the first method step VI the context DSKXT of the data record DS is first determined in step V1_SEL_KXT using the at least one output parameter DA of the data source. Then, in step V1_SEL_INFO, the at least one suitable information source INFO_KXT is determined as a function of the determined context DSKXT.
  • the appropriate information source INFO_KXT can in step V1_SEL_INFO based on the at least one output parameter and possibly. based on the previously determined context DSKXT either from a predetermined group GINFO of information sources INFO_q are selected, with a j e any information source INFO_q the group GINFO one or several of the previously known data patterns DMUS_m each assigns a predetermined assessment BEW, or can be determined using an artificial neural network KNN25, which is set up and trained to output a suitable information source INFO_KXT based on a context DSKXT or output parameter DA supplied to the network KNN25.
  • Various sources of information INFO_q can, for example. be different files, which in a corresponding data storage on a server o. uh . filed or are stored, or such different files, which are stored on different data storage devices.
  • the various data storage devices can be made available by different operators.
  • One of the output parameters DA can, for example. be an identity ID10 of the data source, the identity ID10 of the data source being determined by first checking whether the data source is trustworthy, in particular based on an identity check using a digital certificate, eg. X . 509 . If the data source is trustworthy, i. H . in the event that the check has shown that the data source is trustworthy, the identity ID10 of the data source is based on central, e.g. in a cloud environment, information stored and/or based on information transmitted by the data source.
  • one of the output parameters DA can be a spatial origin LOCI O of the data set, ie, for example. a location of the data source, the origin LOCI O of the data set being determined based on centrally stored information and/or based on information transmitted by the data source, in particular geotagging information.
  • DA a predetermined use of the data set or a specific ter specified context DSKXT be the data set, so that the determination of the context in step V1_SEL_KXT is reduced to reading out the specified context and thus greater security or Reliability of the assessment determination is given.
  • the data set DS is analyzed by the client in the pattern identification method step V1_MUS of the method itself with regard to the presence of a data pattern DSMUS from a large number of previously known data patterns in order to identify one of the known data patterns in the data set DS provided adorn .
  • the context DSKXT of the data record DS is also determined using one or more output parameters DA of the data source.
  • At least one suitable information source INFO_KXT is selected from a predetermined group of information sources, with each information source of the group assigning a respective predetermined assessment BEW to one or more of the previously known data patterns.
  • the actual evaluation BEW of the data record DS is finally determined based on the identified data pattern DSMUS from the selected information source INFO_KXT as that evaluation BEW which is assigned there to the identified data pattern DSMUS.
  • a corresponding system for determining an evaluation BEW of a data set of a data packet provided by a data source to a client of the system has a data analyzer which is set up to carry out the method described, comprising the first VI and the second method step.
  • the data analyzer can have an artificial neural network that is set up and pre-trained in order to determine the assessment BEW of the data set based on the determined data pattern DSMUS and on the determined context DSKXT.
  • the evaluation BEW determined with the system using the method can, for example Provide information about the extent to which the data record DS in the data packet D can be trusted and whether it is reliable, so that consequently reliable decisions can be made.
  • the evaluation BEW of the data record DS can be of different nature depending on the context DSKXT or include different aspects.
  • the assessment BEW of the data set DS can be determined, for example. be understood as a benchmark for the relevance of the value for a specific intended use of the assessed data set DS, for example. for training artificial neural networks by using the evaluation BEW for weighting the data DS for training the network, or for determining the status of an industrial plant and also in completely different fields such as e.g. for calculating prices, costs or taxes when transferring the evaluated data set.
  • the evaluation BEW of the data record DS could be determined by how representative the data record DS is for the later application of the ANN.
  • the ANN is intended for specific applications, e.g. to identify specific objects in an image.
  • a valuable data set DS could include labeled images which show at least some objects that are of interest for the application.
  • the BEW score is lower than for the data set DS with the former images.
  • a record DS can thus be weighted differently depending on the evaluation BEW determined when training the ANN.
  • the evaluation BEW is used to determine the condition of an industrial plant
  • the data set DS is analyzed and, depending on the result of the analysis, i. H . depending on the determined evaluation of the data set DS, the condition of the system is concluded, e.g. "normal” or "faulty” .
  • An industrial plant can, for example, can also be a single machine that works as a component of an IoT architecture.
  • this value BEW can be, for example. serve to set a price for the transmission of the data set DS from a first to a second user, d. H . to determine a type of purchase price, or any tax burden associated with the transfer, etc.
  • FIG. 1 shows an architecture of a data source and a client between which a data packet is transmitted
  • FIG. 2 shows a schematic representation of the method for determining the evaluation BEW.
  • Data source 10 and client 20 can be located at different locations SI, S2, for example in different countries, so that the data transfer may also take place across national borders G.
  • Data source 10 and client 20 can, for example, each be in the form of a data center, a cloud service or the like.
  • the data source 10 can, for example. Technician, another user or a control system or the like, which remotely uses or evaluates the respective provided data packet D, e.g. to monitor, control, maintain the loT device 10 or to the provided data of the data packet D to further process for any purpose. It is also possible, for example, to carry out remote services or predictive maintenance based on information from the data D, with the named user being selected and trained according to the intended use of the data packets D.
  • the provided data packets D are received by the client 20 at the location S2. According to the invention, the client 20 is set up to determine an evaluation BEW of a respective data packet D. E.g.
  • the data packets D are to be processed in one of the other services mentioned above as an example, symbolized in FIG from the client 20 received data packets D or. eg . copies thereof to a data analyzer 23 which carries out the determination of the BEW score.
  • the data broker 21 can, for example. be integrated into a reverse proxy.
  • the function 22 and also the data broker 21 can be dispensed with and the data packets D go directly to the data analyzer after receipt by the client 20 23 .
  • the method executed by the data analyzer 23 for determining the evaluation BEW requires, as explained below and shown in FIG. 2, in addition to the data set DS contained in the data packet D, the output parameter(s) DA also provided there. Furthermore, information from a sample database 24 and from an information database 25 is required, with the sample database 24 and/or the information database 25 being integrated into the data analyzer 23 or organized separately, for example centrally in a cloud.
  • a group GCHAR of characteristics CHAR1, . . . , CHARn with n>1 that are typical for the data packet is determined.
  • the group GCHAR accordingly includes at least one such characteristic CHAR1.
  • the group GCHAR comprises two characteristics CHARI , CHAR2 .
  • the first characteristic CHAR1 is a pattern DSMUS recognizable in the data record DS.
  • the pattern DSMUS can be e.g. be an error code or other recognizable data behavior, e.g. certain temporal behavior within a respective time window. So if e.g. If data is transmitted with a specific pattern or in a specific order within a specific time window, this can be the case, for example. be representative for a specific service case and the data set DS must be evaluated accordingly.
  • the previously known data patterns DMUS_m of the plurality GDMUS are stored in the pattern database 24 already mentioned.
  • the pattern database 24 is designed as a pre-trained artificial neural network KNN24.
  • the use of an artificial neural network KNN24 allows the data pattern DSMUS to be determined even for previously unknown data patterns in the data record DS.
  • the network KNN24 can be trained in advance in the usual way, for example. based on artificial or real data sets DS, for which the patterns DSMUS occurring therein are already known. For the training, the data sets DS are labeled with their respective known patterns DSMUS and the network KNN24 is trained with these labeled data sets in a manner known per se.
  • the first method step VI supplies the first characteristic CHAR1 in its pattern identification method step V1_MUS both in the first and in the second variant, which characteristic represents a data pattern DSMUS in the data record DS.
  • the first method step VI ideally also supplies a second characteristic CHAR2, which is determined in a selection method step V1_SEL of the first method step VI.
  • a context DSKXT of the data record DS is first determined in the selection method step V1_SEL in a step V1_SEL_KXT, with this context DSKXT being determined using the at least one output parameter DA of the data packet D.
  • the context DSKXT can e.g. be a use of the data set DS that is targeted by the client 20 or specified by the data source 10 and/or, for example. the background to the generation of the data satzes DS describe . If e.g.
  • the data source 10 is a nuclear power plant and the data set DS represents current operating parameters of the power plant
  • the context DSKXT could be the operational safety of the power plant and the assessment BEW is a measure of the reliability of the transmitted data DS.
  • the context DSKXT would be this application and the evaluation would be a financial value of the data record DS.
  • the one or more output parameters DA of the data packet D can, for example. metadata regarding the generation and / or regarding. an intended use or evaluation of the data set DS at the client 20 and accordingly written by the data source 10 before transmission to the client 20 in the data packet D.
  • E.g. can one of the several output parameters DA for determining the context DSKXT represent an identity ID10 of the data source 10, the identity ID10 being determined in step V1_SEL_KXT by first checking whether the data source 10 is trustworthy, for example. by means of an identity check using a digital certificate, e.g. X . 509 . With trustworthy data source 10, i. H .
  • the identity ID10 of the data source 10 is based on e.g. centrally in a cloud o . uh . stored information and / or based on information transmitted from the data source 10 determined.
  • one of the several output parameters DA for determining the context DSKXT be a spatial origin LOC10 of the data set 10, ie, for example. the location S 1 of the data source 10, the origin of the data set DS in step V1_SEL_KXT in turn based on centrally stored information and / or based on information transmitted by the data source 10, for example. Geo-tagging information can be determined. If you know the identity ID10 and/or the origin LOCI O, for example. The context DSKXT corresponding to this situation LOCI 0 , ID10 is read out from a corresponding allocation table which in each case assigns a context to various combinations of ID 10 and/or LOCI 0 .
  • output parameters DA be a specific use of the data record DS specified by the data source 10 . It is also conceivable that the output parameter or parameters DA already include the context DSKXT to be used itself.
  • step V1_SEL_KXT Based on the so in step V1_SEL_KXT depending on identity ID10 and / or location LOCI O of the data source 10 and / or possibly.
  • a respective information source INFO_q of the group GINFO assigns a predetermined evaluation BEW to at least one, but typically to a large number of the previously known data patterns DMUS_m. It is quite conceivable that different information sources INFO_ql, INFO_q2 the same data pattern, for example.
  • DMUS_1 assign different ratings BEW1, BEW1, which consequently means that different ratings BEW1, BEW2 could be determined for the same data pattern DMUS_1, depending on the context.
  • Various sources of information INFO_q can, for example. be different files, which are stored in a corresponding data store on a server or . uh . filed or are stored, or such different files, which are stored on different data storage devices.
  • the various data storage devices can be made available by different operators.
  • the previously known sources of information are stored INFO_q in the aforementioned information database 25, with the information database 25 in this first variant being associated with one of the information sources INFO_q for each conceivable context, for example in the form of a table, e.g. a so called "Look-up table" LUT. Accordingly, the information database 25 supplies the data analyzer 23 with the playback of the determined context DSKXT a specific information source INFO_KXT suitable for determining the assessment BEW with 1 ⁇ KXT ⁇ Q from the multitude of information sources INFO_q In the first variant, the information database 25 is based on past experience, ie based on previous assignments of suitable information sources to specific contexts, for example.
  • the information database 25 is designed as a pre-trained artificial neural network KNN25.
  • the use of the artificial neural network KNN25 instead of the table LUT allows a suitable information source INFO_KXT to be determined even for previously unknown contexts DSKXT.
  • the network KNN25 can also be trained in advance in the usual way, for example. based on artificial or real contexts, for which it is known which information source is most suitable, in order to finally obtain the BEW evaluation average .
  • the contexts are labeled with a suitable information source and the network KNN25 is trained with these labeled contexts in a manner known per se.
  • the pattern identification process step V1_MUS for determining CHAR1 and the selection process step V1_SEL for determining CHAR2 can be carried out simultaneously in the first process step VI or in any order one after the other. Only the steps V1_SEL_KXT and V1_SEL_INFO of the selection process step V1_SEL must be executed one after the other, since the step V1_SEL_INFO requires the result of the step V1_SEL_KXT.
  • a predetermined evaluation BEW is assigned to each data pattern DMUS_m in each respective information source INFO_q, as already described above, for example in the form of a corresponding table.
  • the use of an artificial neural network KNN is also conceivable here, which based on data pattern DSMUS and information source INFO_KXT or, al- alternatively, based on the data pattern DSMUS and context DSKXT passed to it, the assessment BEW is determined.
  • the method for determining the evaluation BEW of the data record DS therefore assumes that the evaluation BEW depends on the context DSKXT and on the pattern DSMUS of the data record DS of the data packet D.
  • the data analyzer 23 can be configured as an artificial neural network KNN23, so that some or even all of the individual steps VI, V1_MUS, V1JSEL, V1_SEL_KXT, V1_SEL_INFO and/or V2 described above are no longer executed separately as individual steps, but in one common calculation performed by the ANN23 .
  • step V1_MUS it is advantageous to carry it out with the help of the network KNN24, as already described, since it cannot be ruled out that the pattern DSMUS in the data record DS is not always clearly recognizable. In such cases, the use of an artificial neural network can be helpful.
  • KNN23 requires the data record DS and the output parameters DA as input data in order to determine the assessment BEW.
  • the training of a correspondingly designed artificial neural network which carries out the respective targeted steps or partial steps, can take place as already described above for ANN24 and ANN25 and is based in principle on the approaches known per se for training artificial neural networks.
  • Technical use cases for the method presented here for determining the assessment BEW of a data set DS or for further use of the evaluation BEW itself have already been mentioned above.
  • a further use opens up in that the BEW evaluation actually has a financial value of the data set or of the data packet D represents. This application is particularly advantageous when the data packet D is transmitted across the indicated national border G, so that taxation based on the value of the data becomes necessary.
  • This method enables an automated and therefore reproducible and ef fi cient valuation in such scenarios.

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  • Engineering & Computer Science (AREA)
  • Computer Networks & Wireless Communication (AREA)
  • Signal Processing (AREA)
  • Computer Security & Cryptography (AREA)
  • Computer Hardware Design (AREA)
  • Computing Systems (AREA)
  • General Engineering & Computer Science (AREA)
  • Computer Vision & Pattern Recognition (AREA)
  • Artificial Intelligence (AREA)
  • Databases & Information Systems (AREA)
  • Evolutionary Computation (AREA)
  • Medical Informatics (AREA)
  • Software Systems (AREA)
  • Data Mining & Analysis (AREA)
  • Data Exchanges In Wide-Area Networks (AREA)
  • Management, Administration, Business Operations System, And Electronic Commerce (AREA)

Abstract

L'invention concerne le procédé de détermination d'une évaluation BEW d'un ensemble de données DS d'un paquet de données D. Pour déterminer une évaluation, un modèle de données DSMUS est d'abord identifié dans l'ensemble de données DS. Dans une autre étape indépendante de celle-ci, on détermine un contexte DSKXT qui décrit par exemple les antécédents du processus de génération de l'ensemble de données DS ou une utilisation ciblée de l'ensemble de données DS. L'évaluation BEW à déterminer est basée finalement sur le modèle de données DSMUS ainsi que dans le contexte DSKXT.
EP22705728.8A 2021-02-03 2022-01-28 Détermination d'une évaluation d'un ensemble de données Pending EP4260533A1 (fr)

Applications Claiming Priority (2)

Application Number Priority Date Filing Date Title
DE102021200995.6A DE102021200995A1 (de) 2021-02-03 2021-02-03 Ermittlung einer Bewertung eines Datensatzes
PCT/EP2022/052121 WO2022167345A1 (fr) 2021-02-03 2022-01-28 Détermination d'une évaluation d'un ensemble de données

Publications (1)

Publication Number Publication Date
EP4260533A1 true EP4260533A1 (fr) 2023-10-18

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Application Number Title Priority Date Filing Date
EP22705728.8A Pending EP4260533A1 (fr) 2021-02-03 2022-01-28 Détermination d'une évaluation d'un ensemble de données

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US (1) US20240121234A1 (fr)
EP (1) EP4260533A1 (fr)
CN (1) CN116848829A (fr)
DE (1) DE102021200995A1 (fr)
WO (1) WO2022167345A1 (fr)

Family Cites Families (6)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
US20160314185A1 (en) 2015-04-27 2016-10-27 Microsoft Technology Licensing, Llc Identifying events from aggregated device sensed physical data
US10706533B2 (en) 2016-05-13 2020-07-07 National Jewish Health Systems and methods for automatic detection and quantification of pathology using dynamic feature classification
DE102016217191A1 (de) * 2016-09-09 2018-03-15 Audi Ag Verfahren zum Auswählen und Bewerten von einer Vielzahl von Datensätzen aus zumindest einer Datenquelle
AU2018295566A1 (en) * 2017-07-04 2020-01-23 GeoInt-SafeNet Pty Ltd A system and method for automatically generating geographic specific data
US10474977B2 (en) * 2017-10-27 2019-11-12 International Business Machines Corporation Cognitive learning workflow execution
DE102019203205A1 (de) * 2019-03-08 2020-09-10 Audi Ag Verfahren zum Auswerten von Fahrzeugdaten sowie Fahrzeugdatenauswertesystem zum Durchführen eines derartigen Verfahrens

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CN116848829A (zh) 2023-10-03
WO2022167345A1 (fr) 2022-08-11
DE102021200995A1 (de) 2022-08-04
US20240121234A1 (en) 2024-04-11

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